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The causal relationship between money and income (output) has been an important topic and has been extensively studied. However, those empirical studies are almost entirely on Granger-causality in the conditional mean. Compared to conditional mean, conditional quantiles give a broader picture of an economy in various scenarios. In this paper, we explore whether forecasting conditional quantiles of output growth can be improved using money growth information. We compare the check loss values of quantile forecasts of output growth with and without using past information on money growth, and assess the statistical significance of the loss-differentials. Using U.S. monthly series of real personal income or industrial production for income and output, and M1 or M2 for money, we find that out-of-sample quantile forecasting for output growth is significantly improved by accounting for past money growth information, particularly in tails of the output growth conditional distribution. On the other hand, money–income Granger-causality in the conditional mean is quite weak and unstable. These empirical findings in this paper have not been observed in the money–income literature. The new results of this paper have an important implication on monetary policy, because they imply that the effectiveness of monetary policy has been under-estimated by merely testing Granger-causality in conditional mean. Money does Granger-cause income more strongly than it has been known and therefore information on money growth can (and should) be more utilized in implementing monetary policy.

The purpose of this paper is twofold. First, the paper examines the risk transmission between crude oil and petroleum product prices of Japan’s oil futures market. Second, it compares the performance of two tests for Granger causality using realized variance (RV) and the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model.

The author measures the daily RV of crude oil, kerosene and gasoline futures listed on the Tokyo Commodity Exchange using high-frequency data, and he examines the Granger causality in variance between these variables using the vector autoregression model. Further, the author estimates the EGARCH model based on daily data and test for Granger causality in variance between commodity futures using Hong’s (2001) approach.

The results of the RV approach reveal that the hypothesis on the existence of a mutual volatility spillover between crude oil and petroleum product markets is accepted. However, the results of the conventional approach indicate that all the hypotheses on Granger causalities in variance are rejected. The methodology based on intraday high-frequency data exhibits higher power than the conventional approach based on daily data.

This is the first paper to investigate Japan’s oil market using RV. The authors conclude that the approach based on RV is universally adoptable when testing for Granger causality in variance.

This study probes the psychological interplay between investor sentiment and the returns of cryptocurrencies Bitcoin and Ethereum. Employing the Granger causality test, the authors aim to gauge how extensively the Fear and Greed Index (FGI) can predict cryptocurrency return movements, exploring the intricate bond between investor emotions and market behavior.

The authors used the Granger causality test to achieve research objectives. Going beyond conventional linear analysis, the authors applied Smooth Quantile Regression, scrutinizing weekly data from July 2022 to June 2023 for Bitcoin and Ethereum. The study focus was to determine if the FGI, an indicator of investor sentiment, predicts shifts in cryptocurrency returns.

The study findings underscore the profound psychological sway within cryptocurrency markets. The FGI notably predicts the returns of Bitcoin and Ethereum, underscoring the lasting connection between investor emotions and market behavior. An intriguing feedback loop between the FGI and cryptocurrency returns was identified, accentuating emotions' persistent role in shaping market dynamics. While associations between sentiment and returns were observed at specific lag periods, the nonlinear Granger causality test didn't statistically support nonlinear causality. This suggests linear interactions predominantly govern variable relationships. Cointegration tests highlighted a stable, enduring link between the returns of Bitcoin, Ethereum and the FGI over the long term.

Despite valuable insights, it's crucial to acknowledge our nonlinear analysis's sensitivity to methodological choices. Specifics of time series data and the chosen time frame may have influenced outcomes. Additionally, direct exploration of macroeconomic and geopolitical factors was absent, signaling opportunities for future research.

This study enriches theoretical understanding by illuminating causal dynamics between investor sentiment and cryptocurrency returns. Its significance lies in spotlighting the pivotal role of investor sentiment in shaping cryptocurrency market behavior. It emphasizes the importance of considering this factor when navigating investment decisions in a highly volatile, dynamic market environment.

This study aims to investigate the existence of bubbles and their contagion effect in crude oil and stock markets of oil-exporting countries Gulf Cooperation Council (GCC) from 2016 to 2021.

The authors use Generalized Sup augmented Dickey–Fuller (GSADF) and Backward Sup augmented Dickey–Fuller (BSADF) to significantly identify multiple bubbles stock and oil markets with precise dates. Furthermore, the authors check the contagion effect of bubbles between crude oil and GCC stock markets based on the time-varying Granger causality test.

First, the authors find empirical evidence of downwards bubbles in crude oil prices and in all GCC stock indexes (except the Saudi stock index) during the corona virus disease 2019 (COVID-19) outbreak. Second, the authors do not detect empirical evidence of bubble transmission between crude oil markets and GCC stock markets (except with the Dubai Financial Market index).

The findings of this study would illuminate policymakers not to limit the factors of systematic financial crises in oil-exporting countries to crude oil and to consider factors such as monetary policy and economic diversification measures. This study has also crucial implications for investors. In fact, investors should not ignore the responses of the stock markets to oil price shocks that are heterogeneous across countries when looking for investment opportunities in the GCC region.

The study justifies the changing nature of the bubble contagion effect through the novel implementation of the time-varying Granger causality test to detect whether bubble contagion exists between oil and GCC stock markets and if that does, in which direction.

To explore the state of this conditional Granger causality when other cities are not factors, we investigate housing market networks in China's major cities by using a combination of conditional Granger causality and network analysis.

Although housing market networks have been well discussed for different countries, the question of housing market networks in China's major cities based on the conditional causality perspective has yet to be answered.

We discover that second-tier cities are more influential than first-tier cities. Although the connectivity of the primary housing market is more complex than the diversified connectivity observed in the secondary housing market, both markets are scale-free networks that exhibit high stability. Moreover, we reveal that geographic conditions and economic development jointly determine the housing market's modular hierarchical structure. Our results provide meaningful information for both Chinese policymakers and investors.

By excluding the influence of other cities, our conditional Granger causality identifies the true casual relation between cities' housing markets. Moreover, it is the first paper to consider the primary housing market and secondary housing market separately. Specifically, Chinese prefer new house rather than second-hand house from both speculative and self-housing. Generally speaking, the new house price is lower than the second-hand house price since the new house is off-plan property. Therefore, understanding the difference between primary and secondary housing markets will provide useful information for both policymakers and speculators.

The purpose of this paper is to investigate both linear and/or nonlinear effects of inflation on income inequality and to test the Kuznets hypothesis using panel data of 24 developed countries (DCs) and 66 developing countries (LDCs) observed over the period of 1990–2014.

This paper explores the short- and long-run Granger causality relationship between inflation and income inequality using the Toda and Yamamoto (1995) procedure and a Vector Error Correction Model (VECM) approach. The existence of a nonlinear relationship between inflation and income inequality is confirmed implying as inflation rises income inequality decreases. Income inequality then reaches a minimum and then starts rising again. The findings of this paper show the existence of Kuznets “U-shaped” hypothesis between income inequality and real GDP per capita in DCs group, and the existence of Kuznets’ inverted “U-shaped” hypothesis for LDCs group.

The results indicate that there is no bi-directional Granger causality between inflation and income inequality in the short-run, but, there is bi-directional Granger causality in the long-run for both the DCs and LDCs group. The results help us to assess the effectiveness of monetary policy in reducing income inequality in both the DCs and LDCs group. As a policy implication, monetary policy is often aimed at controlling the annual rate of inflation in the long-run with a short-run focus on reducing output gaps and creating employment. However, managing inflation may have implications for income inequality.

This is original research paper which analyzes the “U-shaped” and inverted “U-shaped” paths of income inequality and real GDP per capita for large sample of two group countries including developed and developing countries, respectively. Also, this paper analyzes the nonlinear relationship between inflation and income inequality in two groups. Furthermore, this paper investigates the short- and long-run relationship between variables. The results are important for policy makers.

The purpose of this paper is to investigate the dynamic relationship among Gold, Crude oil, Indian Rupee-US Dollar and Stock market-Sensex (gold, oil, dollar and stock market (GODS)) in the pre-crisis, the crisis and the post-crisis periods in the Indian context.

The authors use Johansen’s cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression, VEC Granger Causality/Block Exogeneity Wald Test, and Granger Causality and Toda Yamamoto modified Granger causality to study long-run relationship and causality.

Johansen’s cointegration test results indicate that there is a long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period. During the crisis period, both crude oil and Sensex models show long-run causality. However, in some cases, results indicate short-run causality. The authors find one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, the authors conclude that the relationship among GODS is dynamic across global financial crisis.

The research findings of this study are vital to the large group of stakeholders and participants of gold, crude oil, US dollar and stock market in emerging economies like India. The results are useful to importers, exporters, government, policy makers, corporate houses, retail investors, portfolio managers, commodity traders, treasury and fund managers, other commercial traders, etc.

This study is one of its kinds as it investigates the relationship among GODS in India in different sub-periods like before, during and after the global financial crisis of 2008. None of the studies compare phase-wise relationship among GODS in the Indian context. The study contributes to the economic theory and the body of knowledge. It highlights the need to revisit the economic theory to explain the interplay mechanism among GODS.

This paper seeks to study the impact of bio‐fuel policies on oil and food futures prices from December 6, 2004 to August 1, 2008.

The daily closing prices of brent crude oil, light sweet crude oil, corn, wheat, soybeans, and rough rice futures from December 6, 2004 to August 1, 2008 are used in this research. The vector error correction model is applied in order to study the impact of bio‐fuel policies on oil and agricultural futures prices.

Unit root and cointegration tests show that the brent crude oil, light sweet crude oil, wheat, corn, soybeans, and rough rice futures are stationary and have a long‐run equilibrium relationship. Granger causality tests of the four periods shows that the causality relationship between oil futures and food futures changes over time. The first period result shows many Granger causes on several variables at a 5 percent significance level. The second period has more Granger causes at the 5 percent significance level. However, the Granger causality relationships become fewer and fewer in the third and fourth period.

This is the first paper to study the impact of the four major bio‐fuel policies of Brazil, the European Union, and the USA.

Since the famous tapering talk of Bernanke, US Dollar (USD) made a significant appreciation on emerging market local currencies. When the stock indices are adjusted to USD, a negative relationship is usually the case. USD index is a natural candidate for measurement of these effects. It is seen that some emerging stock indices exhibit negative causality with USD index in Granger sense. Moreover, the authors take into account rolling correlations of USD index and the relevant stock indices and examine them on the investment horizon beginning from tapering talk. The authors deduce that Granger causality test and correlation results are coherent with each other which sheds light to the famous discussion whether causality implies correlation or vice versa.

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.